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Total Variation Minimization for Scalar/Vector Regularization

dc.contributor.authorDibos, Françoise
dc.contributor.authorKoepfler, Georges
dc.contributor.authorMonasse, Pascal
HAL ID: 172809
ORCID: 0000-0001-9167-7882
dc.date.accessioned2011-06-23T15:34:33Z
dc.date.available2011-06-23T15:34:33Z
dc.date.issued2003
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6590
dc.language.isoenen
dc.subjectFast Level Sets Transform (FLST)en
dc.subjectlevel setsen
dc.subjectTotal Variation (TV) minimizationen
dc.subject.ddc519en
dc.titleTotal Variation Minimization for Scalar/Vector Regularizationen
dc.typeChapitre d'ouvrage
dc.description.abstractenIn this chapter we present regularization by using Total Variation (TV) minimization based on a level set approach. After introducing the problem of regularization and citing related work, we introduce our main tool, the Fast Level Sets Transform (FLST). This algorithm decomposes an image in a tree of shapes which gives us a non-redundant and complete representation of the image. This representation, associated to some theoretical facts about functions of bounded variation, leads us to a TV minimization algorithm based on level sets. We conclude by comparing this approach to other implementation and/or other models and show applications to regularization of gray scale and color images, as well as optical flow.en
dc.identifier.citationpages121-140en
dc.relation.ispartoftitleGeometric Level Set Methods in Imaging, Vision, and Graphicsen
dc.relation.ispartofeditorOsher, Stanley
dc.relation.ispartofeditorParagios, Nikos
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityBerlinen
dc.relation.ispartofdate2003
dc.relation.ispartofpages513en
dc.relation.ispartofurlhttp://dx.doi.org/10.1007/b97541en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.ispartofisbn978-0-387-95488-2en
dc.identifier.doihttp://dx.doi.org/10.1007/0-387-21810-6_7


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